AI/ML Engineer For Medical Image processing - Bengaluru / Bangalore - Suzva Software Technologies

    Suzva Software Technologies
    Suzva Software Technologies Bengaluru / Bangalore

    5 days ago

    Full time
    Description

    We are seeking a skilled AI/ML Engineer to develop and integrate advanced deep learning solutions into our medical imaging platform. You will work on high-impact use cases such as analyzing MRI scans to assist in Alzheimer's prediction and diagnosis, ensuring your models meet clinical accuracy and performance standards.

    Key Responsibilities:

    • Develop and fine-tune deep learning models (CNNs, ANNs, Transformers) for medical image classification, segmentation, and anomaly detection.
    • Handle medical imaging data formats (e.g., DICOM, NIfTI) for preprocessing and feature extraction.
    • Integrate trained AI models into existing medical imaging platforms using industry-standard deployment pipelines.
    • Collaborate with radiologists, data scientists, and software engineers to validate model performance and usability.
    • Implement model monitoring and update strategies post-deployment to ensure reliability and compliance.
    • Document methodologies, model architecture decisions, and deployment workflows.

    Required Skills & Qualifications:

    • 4+ years of experience in AI/ML, with at least 2 years focused on medical or biomedical image processing.
    • Strong hands-on experience with deep learning frameworks: TensorFlow, PyTorch, Keras.
    • Proficient in Python, with good working knowledge of SQL for data management.
    • Experience working with medical imaging libraries and toolkits such as MONAI, OpenCV, SimpleITK, and scikit-image.
    • Familiarity with cloud-based AI deployment tools and services on AWS, GCP, or Azure.
    • Solid understanding of image annotation, model evaluation metrics (e.g., Dice coefficient, ROC-AUC), and regulatory implications in healthcare AI.

    Preferred Skills (Nice to Have):

    • Experience with federated learning, explainable AI (XAI), or multimodal learning in healthcare.
    • Exposure to containerization (Docker) and MLOps practices for scalable AI deployments.
    • Familiarity with PACS systems, FHIR, or integration with HL7 standards.
    • Knowledge of FDA/CE regulatory requirements for AI in medical devices.
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